Using Deep Learning To Classify Emotions in Tibetan Monks

Loos, Pim van der (2020) Using Deep Learning To Classify Emotions in Tibetan Monks. Bachelor's Thesis, Artificial Intelligence.

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Abstract

Monastic debate is a form of practice that plays an important role in the training of Tibetan Buddhist monks. The goal of these debates is to deepen the monk's understanding of their study materials and the world. During these debates, many emotions such as anger and happiness arise, which makes this a good environment to study naturally occurring emotions. For this study, the electroencephalography (EEG) data for both participants and the videos of 46 debates were recorded. After manually annotating the debates for anger and happiness, two deep learning algorithms were used to classify the emotions of happiness and anger from the EEG data in a subject-independent approach. A long short-term memory (LSTM) and a 1-dimensional convolutional neural network (1D CNN) were used. The LSTM achieved the highest accuracy at 91.0%, with the 1D CNN following at 88.8%. These findings show that deep learning can be used to create a robust classifier for anger and happiness.

Item Type:	Thesis (Bachelor's Thesis)
Supervisor name:	Vugt, M.K. van and Kaushik, P.
Degree programme:	Artificial Intelligence
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	11 Aug 2020 14:48
Last Modified:	11 Aug 2020 14:48
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/23058

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